Abstract
In this paper, combined with water quality sampling data and Landsat8 satellite remote sensing image data, the inversion model of Chl-a and TN water quality parameter concentration was constructed based on machine learning algorithm. After the verification and evaluation of the inversion results of the test samples, Chl-a TN inversion model with high correlation between model test results and measured data was selected to participate in remote sensing inversion ensemble modelling of water quality parameters. Then, the ensemble remote sensing inversion model of water quality parameters was established based on entropy weight method and error analysis. By applying the idea of ensemble modelling to remote sensing inversion of water quality parameters, the advantages of different models can be integrated and the precision of water quality parameters inversion can be improved. Through the evaluation and comparative analysis of the model results, the entropy weight method can improve the inversion accuracy to some extent, but the improvement space is limited. In the verification of the two methods of ensemble modelling based on error analysis, compared with the optimal results of a single model, the determination coefficient (R2) of Chlorophyll a and TN concentration inversion results was increased from 0.9288 to 0.9313 and from 0.8339 to 0.8838, and the root mean square error was decreased from 14.2615 μ/L to 10.4194 μ/L and from1.1002mg/L to 0.8621mg/L. At the same time, with the increase of the number of models involved in the set modelling, the inversion accuracy is higher.
Highlights
Water quality monitoring methods for inland lakes can be divided into conventional detection methods and remote sensing detection methods [1]
In the field of hydrological forecasting, the method of ensemble modelling is often used to synthesize the results of different models to get the optimal solution [6].The method of set modelling is applied to remote sensing inversion to improve the accuracy of water quality parameter inversion on the basis of improving the stability of the model
The ensemble modelling method based on concentration gradient error classification performs best in improving the inversion accuracy of the model
Summary
Water quality monitoring methods for inland lakes can be divided into conventional detection methods and remote sensing detection methods [1]. In the process of remote sensing monitoring of water quality in inland lakes, different inversion models show different applicability and significant along with the spatio-temporal variation of inland water bodies, different inversion data sources and different inversion parameter objects [3,4,5] It is a difficult problem in remote sensing inversion field that how to use different models effectively and reasonably, integrate the simulation results of different models, and get an optimal solution that is closer to the objective real value in the face of the diversity and regional limitations of the model and the complexity of the optical features of inland water. The precision of water quality parameter inversion can be improved by integrating the inversion results of multiple models
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